An Approach to Determine the Number of Clusters for Clustering Algorithms

When clustering a dataset, the right number k of clusters is not often obvious. And choosing k automatically is a complex problem. This paper first reviews existing methods for selecting the number of clusters for the algorithm. Then, an improved algorithm is presented for learning k while clustering. The algorithm is based on coefficients α, β that affect this selection. Meanwhile, a new measure is suggested to confirm the member of clusters. Finally, we evaluate the computational complexity of the algorithm, apply to real datasets and results show its efficiency.